ESPO: Error-Structured Prompt Optimization via Diagnose, Diversify, and Stabilize
Making AI prompts shorter, smarter, and more reliable
A new method called ESPO improves how AI systems are optimized through text instructions, achieving 3.76 percentage points higher accuracy than previous approaches while cutting prompt length by nearly half. The key innovation is breaking optimization into three distinct phases—identifying error patterns, generating diverse candidate solutions, and selecting the most stable ones—which prevents the bloat that plagued earlier methods where longer prompts didn't actually work better.
Shorter prompts mean faster and cheaper AI inference—critical when running language models at scale. The method works reliably across different AI models and tasks, from math problems to question-answering, making it immediately practical for companies deploying these systems. Better accuracy with less computational overhead directly reduces both development time and operational costs.